Overview

Dataset statistics

Number of variables20
Number of observations20631
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory168.0 B

Variable types

Numeric19
Categorical1

Alerts

(Bleed Enthalpy) - s17 is highly overall correlated with (Bypass Ratio) - s15 and 12 other fieldsHigh correlation
(Bypass Ratio) - s15 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(Corrected Core Speed) (rpm) - s14 is highly overall correlated with (Physical Core Speed) (rpm) - s9High correlation
(Corrected Fan Speed) (rpm) - s13 is highly overall correlated with (Bleed Enthalpy) - s17 and 11 other fieldsHigh correlation
(HPC Outlet Pressure) (psia) - s7 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(HPC Outlet Static Pressure) (psia) - s11 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(HPC Outlet Temperature) (â—¦R) - s3 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(High-Pressure Turbines Cool Air Flow) - s20 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(LPC Outlet Temperature) (â—¦R) - s2 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(LPT Outlet Temperature) (â—¦R) - s4 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(Low-Pressure Turbines Cool Air Flow) - s21 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(Physical Core Speed) (rpm) - s9 is highly overall correlated with (Corrected Core Speed) (rpm) - s14High correlation
(Physical Fan Speed) (rpm) - s8 is highly overall correlated with (Bleed Enthalpy) - s17 and 11 other fieldsHigh correlation
(Ratio of Fuel Flow to Ps30) (pps/psia) - s12 is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
Cycle is highly overall correlated with (Bleed Enthalpy) - s17 and 10 other fieldsHigh correlation
RUL is highly overall correlated with (Bleed Enthalpy) - s17 and 12 other fieldsHigh correlation
(Bypass-Duct Pressure) (psia) - s6 is highly imbalanced (86.0%)Imbalance
Setting 1 - c1 has 413 (2.0%) zerosZeros
Setting 2 - c2 has 2070 (10.0%) zerosZeros

Reproduction

Analysis started2024-05-24 14:55:32.524107
Analysis finished2024-05-24 14:56:05.581949
Duration33.06 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Engine
Real number (ℝ)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.506568
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:05.677480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median52
Q377
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.227633
Coefficient of variation (CV)0.56745449
Kurtosis-1.2198241
Mean51.506568
Median Absolute Deviation (MAD)26
Skewness-0.067815234
Sum1062632
Variance854.25453
MonotonicityIncreasing
2024-05-24T18:26:05.808406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 362
 
1.8%
92 341
 
1.7%
96 336
 
1.6%
67 313
 
1.5%
83 293
 
1.4%
2 287
 
1.4%
95 283
 
1.4%
64 283
 
1.4%
86 278
 
1.3%
17 276
 
1.3%
Other values (90) 17579
85.2%
ValueCountFrequency (%)
1 192
0.9%
2 287
1.4%
3 179
0.9%
4 189
0.9%
5 269
1.3%
6 188
0.9%
7 259
1.3%
8 150
0.7%
9 201
1.0%
10 222
1.1%
ValueCountFrequency (%)
100 200
1.0%
99 185
0.9%
98 156
0.8%
97 202
1.0%
96 336
1.6%
95 283
1.4%
94 258
1.3%
93 155
0.8%
92 341
1.7%
91 135
 
0.7%

Cycle
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.80786
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:05.917392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3156
95-th percentile230
Maximum362
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099
Coefficient of variation (CV)0.63305159
Kurtosis-0.2185391
Mean108.80786
Median Absolute Deviation (MAD)52
Skewness0.49990397
Sum2244815
Variance4744.5908
MonotonicityNot monotonic
2024-05-24T18:26:06.032172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
0.5%
66 100
 
0.5%
97 100
 
0.5%
96 100
 
0.5%
95 100
 
0.5%
94 100
 
0.5%
93 100
 
0.5%
91 100
 
0.5%
90 100
 
0.5%
89 100
 
0.5%
Other values (352) 19631
95.2%
ValueCountFrequency (%)
1 100
0.5%
2 100
0.5%
3 100
0.5%
4 100
0.5%
5 100
0.5%
6 100
0.5%
7 100
0.5%
8 100
0.5%
9 100
0.5%
10 100
0.5%
ValueCountFrequency (%)
362 1
< 0.1%
361 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
358 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%

Setting 1 - c1
Real number (ℝ)

ZEROS 

Distinct158
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.8701469 × 10-6
Minimum-0.0087
Maximum0.0087
Zeros413
Zeros (%)2.0%
Negative10061
Negative (%)48.8%
Memory size322.4 KiB
2024-05-24T18:26:06.140565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.0087
5-th percentile-0.0037
Q1-0.0015
median0
Q30.0015
95-th percentile0.0036
Maximum0.0087
Range0.0174
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0021873134
Coefficient of variation (CV)-246.5927
Kurtosis-0.0091316243
Mean-8.8701469 × 10-6
Median Absolute Deviation (MAD)0.0015
Skewness-0.024766267
Sum-0.183
Variance4.7843401 × 10-6
MonotonicityNot monotonic
2024-05-24T18:26:06.249758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
2.0%
0.0002 398
 
1.9%
0.0004 394
 
1.9%
-0.0005 390
 
1.9%
0.0001 382
 
1.9%
0.0005 381
 
1.8%
0.0006 379
 
1.8%
-0.0006 375
 
1.8%
0.0003 364
 
1.8%
0.0009 362
 
1.8%
Other values (148) 16793
81.4%
ValueCountFrequency (%)
-0.0087 1
 
< 0.1%
-0.0086 1
 
< 0.1%
-0.0084 1
 
< 0.1%
-0.0081 2
< 0.1%
-0.0078 1
 
< 0.1%
-0.0075 1
 
< 0.1%
-0.0074 3
< 0.1%
-0.0073 1
 
< 0.1%
-0.0072 2
< 0.1%
-0.007 2
< 0.1%
ValueCountFrequency (%)
0.0087 1
 
< 0.1%
0.0083 1
 
< 0.1%
0.0077 1
 
< 0.1%
0.0076 1
 
< 0.1%
0.0074 3
< 0.1%
0.0073 1
 
< 0.1%
0.0072 4
< 0.1%
0.0071 2
< 0.1%
0.007 2
< 0.1%
0.0069 2
< 0.1%

Setting 2 - c2
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3508313 × 10-6
Minimum-0.0006
Maximum0.0006
Zeros2070
Zeros (%)10.0%
Negative9225
Negative (%)44.7%
Memory size322.4 KiB
2024-05-24T18:26:06.345968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median0
Q30.0003
95-th percentile0.0005
Maximum0.0006
Range0.0012
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00029306212
Coefficient of variation (CV)124.66319
Kurtosis-1.130447
Mean2.3508313 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.0090851197
Sum0.0485
Variance8.5885409 × 10-8
MonotonicityNot monotonic
2024-05-24T18:26:06.436447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-0.0003 2104
10.2%
0.0001 2097
10.2%
0 2070
10.0%
0.0003 2065
10.0%
-0.0004 2051
9.9%
-0.0002 2049
9.9%
0.0002 2038
9.9%
-0.0001 2029
9.8%
0.0004 1997
9.7%
0.0005 1068
5.2%
Other values (3) 1063
5.2%
ValueCountFrequency (%)
-0.0006 34
 
0.2%
-0.0005 958
4.6%
-0.0004 2051
9.9%
-0.0003 2104
10.2%
-0.0002 2049
9.9%
-0.0001 2029
9.8%
0 2070
10.0%
0.0001 2097
10.2%
0.0002 2038
9.9%
0.0003 2065
10.0%
ValueCountFrequency (%)
0.0006 71
 
0.3%
0.0005 1068
5.2%
0.0004 1997
9.7%
0.0003 2065
10.0%
0.0002 2038
9.9%
0.0001 2097
10.2%
0 2070
10.0%
-0.0001 2029
9.8%
-0.0002 2049
9.9%
-0.0003 2104
10.2%

(LPC Outlet Temperature) (â—¦R) - s2
Real number (ℝ)

HIGH CORRELATION 

Distinct310
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.68093
Minimum641.21
Maximum644.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:06.538933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum641.21
5-th percentile641.92
Q1642.325
median642.64
Q3643
95-th percentile643.58
Maximum644.53
Range3.32
Interquartile range (IQR)0.675

Descriptive statistics

Standard deviation0.50005327
Coefficient of variation (CV)0.00077807392
Kurtosis-0.11204294
Mean642.68093
Median Absolute Deviation (MAD)0.34
Skewness0.31652589
Sum13259150
Variance0.25005327
MonotonicityNot monotonic
2024-05-24T18:26:06.649641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.5 190
 
0.9%
642.56 189
 
0.9%
642.53 188
 
0.9%
642.6 184
 
0.9%
642.67 179
 
0.9%
642.44 175
 
0.8%
642.63 175
 
0.8%
642.57 172
 
0.8%
642.64 168
 
0.8%
642.73 167
 
0.8%
Other values (300) 18844
91.3%
ValueCountFrequency (%)
641.21 1
 
< 0.1%
641.25 2
< 0.1%
641.27 3
< 0.1%
641.3 4
< 0.1%
641.31 1
 
< 0.1%
641.32 2
< 0.1%
641.33 2
< 0.1%
641.34 1
 
< 0.1%
641.35 1
 
< 0.1%
641.36 2
< 0.1%
ValueCountFrequency (%)
644.53 2
< 0.1%
644.5 1
< 0.1%
644.47 1
< 0.1%
644.44 1
< 0.1%
644.39 1
< 0.1%
644.37 1
< 0.1%
644.35 1
< 0.1%
644.34 1
< 0.1%
644.31 1
< 0.1%
644.3 2
< 0.1%

(HPC Outlet Temperature) (â—¦R) - s3
Real number (ℝ)

HIGH CORRELATION 

Distinct3012
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1590.5231
Minimum1571.04
Maximum1616.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:06.783589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1571.04
5-th percentile1581.11
Q11586.26
median1590.1
Q31594.38
95-th percentile1601.47
Maximum1616.91
Range45.87
Interquartile range (IQR)8.12

Descriptive statistics

Standard deviation6.1311495
Coefficient of variation (CV)0.0038548006
Kurtosis0.0077618224
Mean1590.5231
Median Absolute Deviation (MAD)4.05
Skewness0.30894581
Sum32814082
Variance37.590994
MonotonicityNot monotonic
2024-05-24T18:26:06.941655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1590.1 27
 
0.1%
1589.76 26
 
0.1%
1589.98 25
 
0.1%
1592.11 25
 
0.1%
1587.86 24
 
0.1%
1584.95 23
 
0.1%
1590.54 23
 
0.1%
1589.08 23
 
0.1%
1589.44 23
 
0.1%
1587.82 22
 
0.1%
Other values (3002) 20390
98.8%
ValueCountFrequency (%)
1571.04 1
< 0.1%
1571.06 1
< 0.1%
1571.84 1
< 0.1%
1571.99 1
< 0.1%
1572.34 1
< 0.1%
1572.4 1
< 0.1%
1572.46 1
< 0.1%
1572.67 1
< 0.1%
1572.76 1
< 0.1%
1572.98 1
< 0.1%
ValueCountFrequency (%)
1616.91 1
< 0.1%
1614.93 1
< 0.1%
1614.72 1
< 0.1%
1613.62 1
< 0.1%
1613.29 1
< 0.1%
1612.88 1
< 0.1%
1612.63 1
< 0.1%
1612.11 1
< 0.1%
1611.92 1
< 0.1%
1611.57 1
< 0.1%

(LPT Outlet Temperature) (â—¦R) - s4
Real number (ℝ)

HIGH CORRELATION 

Distinct4051
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.9338
Minimum1382.25
Maximum1441.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:07.058223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1382.25
5-th percentile1395.62
Q11402.36
median1408.04
Q31414.555
95-th percentile1425.67
Maximum1441.49
Range59.24
Interquartile range (IQR)12.195

Descriptive statistics

Standard deviation9.0006048
Coefficient of variation (CV)0.0063882383
Kurtosis-0.16368086
Mean1408.9338
Median Absolute Deviation (MAD)6.04
Skewness0.44319434
Sum29067713
Variance81.010886
MonotonicityNot monotonic
2024-05-24T18:26:07.173330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1409.01 20
 
0.1%
1404.47 18
 
0.1%
1407.15 18
 
0.1%
1407.02 18
 
0.1%
1414.03 18
 
0.1%
1410.54 18
 
0.1%
1403.23 17
 
0.1%
1407.18 16
 
0.1%
1410.57 16
 
0.1%
1401.27 16
 
0.1%
Other values (4041) 20456
99.2%
ValueCountFrequency (%)
1382.25 1
< 0.1%
1385.19 1
< 0.1%
1385.75 1
< 0.1%
1386.29 1
< 0.1%
1386.43 1
< 0.1%
1386.69 1
< 0.1%
1387.16 1
< 0.1%
1387.36 1
< 0.1%
1387.38 1
< 0.1%
1387.5 1
< 0.1%
ValueCountFrequency (%)
1441.49 1
< 0.1%
1438.96 1
< 0.1%
1438.51 1
< 0.1%
1438.41 1
< 0.1%
1438.22 1
< 0.1%
1438.16 1
< 0.1%
1438.1 1
< 0.1%
1437.98 1
< 0.1%
1437.88 1
< 0.1%
1437.81 1
< 0.1%

(Bypass-Duct Pressure) (psia) - s6
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
21.61
20225 
21.6
 
406

Length

Max length5
Median length5
Mean length4.9803209
Min length4

Characters and Unicode

Total characters102749
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21.61
2nd row21.61
3rd row21.61
4th row21.61
5th row21.61

Common Values

ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Length

2024-05-24T18:26:07.279713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T18:26:07.362671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

(HPC Outlet Pressure) (psia) - s7
Real number (ℝ)

HIGH CORRELATION 

Distinct513
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.36771
Minimum549.85
Maximum556.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:07.453713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum549.85
5-th percentile551.74
Q1552.81
median553.44
Q3554.01
95-th percentile554.69
Maximum556.06
Range6.21
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.88509226
Coefficient of variation (CV)0.0015994649
Kurtosis-0.15794922
Mean553.36771
Median Absolute Deviation (MAD)0.6
Skewness-0.39432894
Sum11416529
Variance0.7833883
MonotonicityNot monotonic
2024-05-24T18:26:07.566017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553.62 116
 
0.6%
553.76 115
 
0.6%
553.72 110
 
0.5%
553.94 110
 
0.5%
553.43 108
 
0.5%
553.74 107
 
0.5%
553.75 106
 
0.5%
554 105
 
0.5%
553.9 104
 
0.5%
553.52 103
 
0.5%
Other values (503) 19547
94.7%
ValueCountFrequency (%)
549.85 1
< 0.1%
550.34 1
< 0.1%
550.35 1
< 0.1%
550.42 1
< 0.1%
550.43 1
< 0.1%
550.48 2
< 0.1%
550.49 1
< 0.1%
550.5 1
< 0.1%
550.51 2
< 0.1%
550.52 1
< 0.1%
ValueCountFrequency (%)
556.06 1
< 0.1%
555.86 1
< 0.1%
555.72 1
< 0.1%
555.7 1
< 0.1%
555.67 1
< 0.1%
555.66 1
< 0.1%
555.64 1
< 0.1%
555.61 1
< 0.1%
555.6 1
< 0.1%
555.58 1
< 0.1%

(Physical Fan Speed) (rpm) - s8
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0967
Minimum2387.9
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:07.678236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2387.9
5-th percentile2387.99
Q12388.05
median2388.09
Q32388.14
95-th percentile2388.22
Maximum2388.56
Range0.66
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.070985479
Coefficient of variation (CV)2.9724709 × 10-5
Kurtosis0.33314901
Mean2388.0967
Median Absolute Deviation (MAD)0.05
Skewness0.47941086
Sum49268822
Variance0.0050389382
MonotonicityNot monotonic
2024-05-24T18:26:07.784720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.11 1181
 
5.7%
2388.1 1159
 
5.6%
2388.09 1149
 
5.6%
2388.08 1126
 
5.5%
2388.07 1077
 
5.2%
2388.12 1069
 
5.2%
2388.06 1050
 
5.1%
2388.13 1033
 
5.0%
2388.05 1013
 
4.9%
2388.04 910
 
4.4%
Other values (43) 9864
47.8%
ValueCountFrequency (%)
2387.9 1
 
< 0.1%
2387.91 3
 
< 0.1%
2387.92 9
 
< 0.1%
2387.93 16
 
0.1%
2387.94 33
 
0.2%
2387.95 72
 
0.3%
2387.96 145
 
0.7%
2387.97 201
1.0%
2387.98 339
1.6%
2387.99 426
2.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.52 1
 
< 0.1%
2388.5 1
 
< 0.1%
2388.46 1
 
< 0.1%
2388.44 2
 
< 0.1%
2388.37 1
 
< 0.1%
2388.36 1
 
< 0.1%
2388.35 2
 
< 0.1%
2388.34 13
0.1%
2388.33 13
0.1%

(Physical Core Speed) (rpm) - s9
Real number (ℝ)

HIGH CORRELATION 

Distinct6403
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9065.2429
Minimum9021.73
Maximum9244.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:07.898015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9021.73
5-th percentile9042.55
Q19053.1
median9060.66
Q39069.42
95-th percentile9109.98
Maximum9244.59
Range222.86
Interquartile range (IQR)16.32

Descriptive statistics

Standard deviation22.08288
Coefficient of variation (CV)0.0024359942
Kurtosis9.3786813
Mean9065.2429
Median Absolute Deviation (MAD)8.13
Skewness2.5553649
Sum1.8702503 × 108
Variance487.65357
MonotonicityNot monotonic
2024-05-24T18:26:08.028863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9058.88 16
 
0.1%
9060.37 15
 
0.1%
9060.55 15
 
0.1%
9056.86 15
 
0.1%
9063.22 15
 
0.1%
9060.87 15
 
0.1%
9054.54 14
 
0.1%
9061.05 14
 
0.1%
9057.95 14
 
0.1%
9065.47 14
 
0.1%
Other values (6393) 20484
99.3%
ValueCountFrequency (%)
9021.73 1
< 0.1%
9023.85 1
< 0.1%
9024.27 1
< 0.1%
9024.42 1
< 0.1%
9025.22 1
< 0.1%
9025.29 1
< 0.1%
9026.08 1
< 0.1%
9026.17 1
< 0.1%
9026.19 1
< 0.1%
9026.66 1
< 0.1%
ValueCountFrequency (%)
9244.59 1
< 0.1%
9239.76 1
< 0.1%
9228.53 1
< 0.1%
9226.6 1
< 0.1%
9224.87 1
< 0.1%
9224.53 1
< 0.1%
9223.56 1
< 0.1%
9221.31 1
< 0.1%
9220.88 1
< 0.1%
9219.81 1
< 0.1%

(HPC Outlet Static Pressure) (psia) - s11
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.541168
Minimum46.85
Maximum48.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:08.150023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum46.85
5-th percentile47.15
Q147.35
median47.51
Q347.7
95-th percentile48.045
Maximum48.53
Range1.68
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.2670874
Coefficient of variation (CV)0.0056180235
Kurtosis-0.17219188
Mean47.541168
Median Absolute Deviation (MAD)0.18
Skewness0.46932909
Sum980821.84
Variance0.071335679
MonotonicityNot monotonic
2024-05-24T18:26:08.272602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.46 341
 
1.7%
47.57 338
 
1.6%
47.49 332
 
1.6%
47.45 332
 
1.6%
47.47 331
 
1.6%
47.52 326
 
1.6%
47.37 321
 
1.6%
47.48 319
 
1.5%
47.44 318
 
1.5%
47.43 311
 
1.5%
Other values (149) 17362
84.2%
ValueCountFrequency (%)
46.85 1
 
< 0.1%
46.86 3
< 0.1%
46.88 2
 
< 0.1%
46.89 1
 
< 0.1%
46.9 1
 
< 0.1%
46.91 1
 
< 0.1%
46.92 3
< 0.1%
46.93 3
< 0.1%
46.94 6
< 0.1%
46.95 6
< 0.1%
ValueCountFrequency (%)
48.53 1
 
< 0.1%
48.52 1
 
< 0.1%
48.48 1
 
< 0.1%
48.43 1
 
< 0.1%
48.41 4
< 0.1%
48.4 4
< 0.1%
48.39 3
< 0.1%
48.38 1
 
< 0.1%
48.37 2
 
< 0.1%
48.35 5
< 0.1%

(Ratio of Fuel Flow to Ps30) (pps/psia) - s12
Real number (ℝ)

HIGH CORRELATION 

Distinct427
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.41347
Minimum518.69
Maximum523.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:08.385960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum518.69
5-th percentile520.04
Q1520.96
median521.48
Q3521.95
95-th percentile522.5
Maximum523.38
Range4.69
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation0.73755339
Coefficient of variation (CV)0.0014145269
Kurtosis-0.14491657
Mean521.41347
Median Absolute Deviation (MAD)0.5
Skewness-0.44240724
Sum10757281
Variance0.54398501
MonotonicityNot monotonic
2024-05-24T18:26:08.502700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.63 143
 
0.7%
521.42 136
 
0.7%
521.35 131
 
0.6%
521.56 129
 
0.6%
521.66 126
 
0.6%
521.54 125
 
0.6%
521.69 124
 
0.6%
521.5 123
 
0.6%
521.46 121
 
0.6%
521.43 121
 
0.6%
Other values (417) 19352
93.8%
ValueCountFrequency (%)
518.69 1
< 0.1%
518.83 2
< 0.1%
518.94 1
< 0.1%
518.95 1
< 0.1%
518.98 1
< 0.1%
518.99 1
< 0.1%
519.01 1
< 0.1%
519.02 1
< 0.1%
519.03 1
< 0.1%
519.06 2
< 0.1%
ValueCountFrequency (%)
523.38 2
< 0.1%
523.35 1
< 0.1%
523.31 1
< 0.1%
523.27 1
< 0.1%
523.26 2
< 0.1%
523.25 1
< 0.1%
523.24 1
< 0.1%
523.23 1
< 0.1%
523.21 1
< 0.1%
523.2 1
< 0.1%

(Corrected Fan Speed) (rpm) - s13
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0962
Minimum2387.88
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:08.611765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2387.88
5-th percentile2387.99
Q12388.04
median2388.09
Q32388.14
95-th percentile2388.23
Maximum2388.56
Range0.68
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.071918916
Coefficient of variation (CV)3.0115586 × 10-5
Kurtosis0.38724376
Mean2388.0962
Median Absolute Deviation (MAD)0.05
Skewness0.46979242
Sum49268812
Variance0.0051723304
MonotonicityNot monotonic
2024-05-24T18:26:08.736802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.1 1164
 
5.6%
2388.09 1144
 
5.5%
2388.08 1129
 
5.5%
2388.11 1127
 
5.5%
2388.07 1112
 
5.4%
2388.12 1099
 
5.3%
2388.06 1005
 
4.9%
2388.05 987
 
4.8%
2388.13 976
 
4.7%
2388.04 952
 
4.6%
Other values (46) 9936
48.2%
ValueCountFrequency (%)
2387.88 1
 
< 0.1%
2387.89 1
 
< 0.1%
2387.9 1
 
< 0.1%
2387.91 2
 
< 0.1%
2387.92 12
 
0.1%
2387.93 19
 
0.1%
2387.94 54
 
0.3%
2387.95 95
0.5%
2387.96 170
0.8%
2387.97 219
1.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.55 1
 
< 0.1%
2388.54 1
 
< 0.1%
2388.49 1
 
< 0.1%
2388.44 1
 
< 0.1%
2388.39 2
 
< 0.1%
2388.37 3
 
< 0.1%
2388.36 6
< 0.1%
2388.35 7
< 0.1%
2388.34 8
< 0.1%

(Corrected Core Speed) (rpm) - s14
Real number (ℝ)

HIGH CORRELATION 

Distinct6078
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8143.7527
Minimum8099.94
Maximum8293.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:08.856714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8099.94
5-th percentile8122.505
Q18133.245
median8140.54
Q38148.31
95-th percentile8181.405
Maximum8293.72
Range193.78
Interquartile range (IQR)15.065

Descriptive statistics

Standard deviation19.076176
Coefficient of variation (CV)0.0023424306
Kurtosis8.8546645
Mean8143.7527
Median Absolute Deviation (MAD)7.54
Skewness2.3725536
Sum1.6801376 × 108
Variance363.90049
MonotonicityNot monotonic
2024-05-24T18:26:08.980864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8138.89 17
 
0.1%
8141.85 17
 
0.1%
8136.89 16
 
0.1%
8140.79 15
 
0.1%
8140.65 15
 
0.1%
8140.49 15
 
0.1%
8140.33 15
 
0.1%
8140.89 15
 
0.1%
8136.69 15
 
0.1%
8140.97 15
 
0.1%
Other values (6068) 20476
99.2%
ValueCountFrequency (%)
8099.94 1
< 0.1%
8101.49 1
< 0.1%
8102.82 1
< 0.1%
8103.27 1
< 0.1%
8103.77 1
< 0.1%
8103.98 1
< 0.1%
8104.46 1
< 0.1%
8104.78 1
< 0.1%
8104.82 1
< 0.1%
8105.22 1
< 0.1%
ValueCountFrequency (%)
8293.72 1
< 0.1%
8290.25 1
< 0.1%
8289.63 1
< 0.1%
8288.26 1
< 0.1%
8282.5 1
< 0.1%
8279.86 1
< 0.1%
8279.79 1
< 0.1%
8276.2 1
< 0.1%
8274.65 1
< 0.1%
8273.15 1
< 0.1%

(Bypass Ratio) - s15
Real number (ℝ)

HIGH CORRELATION 

Distinct1918
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4421456
Minimum8.3249
Maximum8.5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:09.096803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8.3249
5-th percentile8.3859
Q18.4149
median8.4389
Q38.4656
95-th percentile8.511
Maximum8.5848
Range0.2599
Interquartile range (IQR)0.0507

Descriptive statistics

Standard deviation0.037505038
Coefficient of variation (CV)0.0044425955
Kurtosis-0.12143
Mean8.4421456
Median Absolute Deviation (MAD)0.0252
Skewness0.38825858
Sum174169.91
Variance0.0014066279
MonotonicityNot monotonic
2024-05-24T18:26:09.216780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4309 38
 
0.2%
8.4318 37
 
0.2%
8.4468 36
 
0.2%
8.4442 35
 
0.2%
8.4128 34
 
0.2%
8.4453 32
 
0.2%
8.4446 32
 
0.2%
8.4371 31
 
0.2%
8.4209 31
 
0.2%
8.4226 31
 
0.2%
Other values (1908) 20294
98.4%
ValueCountFrequency (%)
8.3249 1
< 0.1%
8.3279 1
< 0.1%
8.3303 1
< 0.1%
8.3358 2
< 0.1%
8.3365 1
< 0.1%
8.3387 1
< 0.1%
8.34 1
< 0.1%
8.3409 1
< 0.1%
8.3427 1
< 0.1%
8.3428 1
< 0.1%
ValueCountFrequency (%)
8.5848 1
< 0.1%
8.5836 1
< 0.1%
8.5678 1
< 0.1%
8.5671 1
< 0.1%
8.5668 1
< 0.1%
8.5665 1
< 0.1%
8.5654 1
< 0.1%
8.5648 1
< 0.1%
8.5646 1
< 0.1%
8.5641 1
< 0.1%

(Bleed Enthalpy) - s17
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.21065
Minimum388
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:09.311690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile391
Q1392
median393
Q3394
95-th percentile396
Maximum400
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.548763
Coefficient of variation (CV)0.0039387616
Kurtosis-0.039174043
Mean393.21065
Median Absolute Deviation (MAD)1
Skewness0.35312566
Sum8112329
Variance2.3986669
MonotonicityNot monotonic
2024-05-24T18:26:09.404542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
393 5445
26.4%
392 4578
22.2%
394 4063
19.7%
395 2339
11.3%
391 2022
 
9.8%
396 1185
 
5.7%
390 452
 
2.2%
397 436
 
2.1%
398 72
 
0.3%
389 30
 
0.1%
Other values (3) 9
 
< 0.1%
ValueCountFrequency (%)
388 1
 
< 0.1%
389 30
 
0.1%
390 452
 
2.2%
391 2022
 
9.8%
392 4578
22.2%
393 5445
26.4%
394 4063
19.7%
395 2339
11.3%
396 1185
 
5.7%
397 436
 
2.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
399 7
 
< 0.1%
398 72
 
0.3%
397 436
 
2.1%
396 1185
 
5.7%
395 2339
11.3%
394 4063
19.7%
393 5445
26.4%
392 4578
22.2%
391 2022
 
9.8%

(High-Pressure Turbines Cool Air Flow) - s20
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.816271
Minimum38.14
Maximum39.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:09.509350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum38.14
5-th percentile38.49
Q138.7
median38.83
Q338.95
95-th percentile39.09
Maximum39.43
Range1.29
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18074643
Coefficient of variation (CV)0.0046564604
Kurtosis-0.11282911
Mean38.816271
Median Absolute Deviation (MAD)0.12
Skewness-0.3584452
Sum800818.48
Variance0.032669271
MonotonicityNot monotonic
2024-05-24T18:26:09.627635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.86 485
 
2.4%
38.89 476
 
2.3%
38.82 472
 
2.3%
38.87 460
 
2.2%
38.85 458
 
2.2%
38.83 457
 
2.2%
38.84 455
 
2.2%
38.88 452
 
2.2%
38.81 447
 
2.2%
38.8 447
 
2.2%
Other values (110) 16022
77.7%
ValueCountFrequency (%)
38.14 1
 
< 0.1%
38.16 1
 
< 0.1%
38.18 1
 
< 0.1%
38.19 1
 
< 0.1%
38.2 1
 
< 0.1%
38.21 1
 
< 0.1%
38.22 3
 
< 0.1%
38.23 5
< 0.1%
38.24 7
< 0.1%
38.25 9
< 0.1%
ValueCountFrequency (%)
39.43 1
 
< 0.1%
39.41 1
 
< 0.1%
39.34 1
 
< 0.1%
39.32 1
 
< 0.1%
39.31 2
 
< 0.1%
39.3 2
 
< 0.1%
39.29 3
 
< 0.1%
39.28 1
 
< 0.1%
39.27 10
< 0.1%
39.26 7
< 0.1%

(Low-Pressure Turbines Cool Air Flow) - s21
Real number (ℝ)

HIGH CORRELATION 

Distinct4745
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.289705
Minimum22.8942
Maximum23.6184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:09.739551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum22.8942
5-th percentile23.09345
Q123.2218
median23.2979
Q323.3668
95-th percentile23.4535
Maximum23.6184
Range0.7242
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation0.10825087
Coefficient of variation (CV)0.0046480139
Kurtosis-0.11703945
Mean23.289705
Median Absolute Deviation (MAD)0.0724
Skewness-0.35037496
Sum480489.91
Variance0.011718252
MonotonicityNot monotonic
2024-05-24T18:26:09.851880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3222 23
 
0.1%
23.3029 17
 
0.1%
23.2896 16
 
0.1%
23.3725 16
 
0.1%
23.371 15
 
0.1%
23.3491 15
 
0.1%
23.3497 15
 
0.1%
23.3315 15
 
0.1%
23.3002 15
 
0.1%
23.3309 15
 
0.1%
Other values (4735) 20469
99.2%
ValueCountFrequency (%)
22.8942 1
< 0.1%
22.9071 1
< 0.1%
22.9122 1
< 0.1%
22.9305 1
< 0.1%
22.9333 1
< 0.1%
22.9337 1
< 0.1%
22.9364 1
< 0.1%
22.9396 2
< 0.1%
22.9398 1
< 0.1%
22.9402 1
< 0.1%
ValueCountFrequency (%)
23.6184 1
< 0.1%
23.6127 1
< 0.1%
23.6064 1
< 0.1%
23.6005 1
< 0.1%
23.5983 1
< 0.1%
23.589 1
< 0.1%
23.5862 2
< 0.1%
23.5858 1
< 0.1%
23.5825 1
< 0.1%
23.5791 1
< 0.1%

RUL
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.80786
Minimum0
Maximum361
Zeros100
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2024-05-24T18:26:09.969892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q151
median103
Q3155
95-th percentile229
Maximum361
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099
Coefficient of variation (CV)0.63892363
Kurtosis-0.2185391
Mean107.80786
Median Absolute Deviation (MAD)52
Skewness0.49990397
Sum2224184
Variance4744.5908
MonotonicityNot monotonic
2024-05-24T18:26:10.086116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 100
 
0.5%
61 100
 
0.5%
70 100
 
0.5%
69 100
 
0.5%
68 100
 
0.5%
67 100
 
0.5%
66 100
 
0.5%
65 100
 
0.5%
64 100
 
0.5%
63 100
 
0.5%
Other values (352) 19631
95.2%
ValueCountFrequency (%)
0 100
0.5%
1 100
0.5%
2 100
0.5%
3 100
0.5%
4 100
0.5%
5 100
0.5%
6 100
0.5%
7 100
0.5%
8 100
0.5%
9 100
0.5%
ValueCountFrequency (%)
361 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
358 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%
352 1
< 0.1%

Interactions

2024-05-24T18:26:03.639560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:33.475548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:35.087485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.842188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.514951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:40.299962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:42.005727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.728294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:45.398053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:47.116019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.728399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.426297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:52.038182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.545704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:55.123728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.996654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.473816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:00.160950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.737358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:03.714550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:33.555644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:35.163198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.922112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.592788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:40.378949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:42.079800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.811525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:45.489831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:47.195855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.806363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.503701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:52.117801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.622988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:55.201681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:57.070121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.548095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:00.238854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.814097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:03.794074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:33.643455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:35.242902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:37.031624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.685319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:40.482172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:42.159849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.893906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:45.594236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:47.280992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.886142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.584693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:52.213224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.702420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:57.145589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:26:03.869876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:37.114512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:42.247028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.971822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:47.365977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:50.664801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:52.291533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.785313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:55.596929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:57.224874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.707289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:26:01.988061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:03.953847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:33.845243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:35.408389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:37.201493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.855964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:40.663270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:42.330197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:52.372903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.872423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:57.303509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:38.948091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:42.418112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:53.956315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:57.383999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:34.012231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:35.572091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:37.410707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:55.899608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:57.452931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.973642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:25:52.760681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.297874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.147229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:57.682727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.285260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:00.903773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:02.494204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:04.437822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.333780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.034108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:37.750048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:39.505337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.206495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:42.854634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:44.532106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.318224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:47.949757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:49.708519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.240620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:52.839963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.376887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.230652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:57.755592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.365923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:00.983548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:02.574728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:04.519835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.414730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.120183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:37.832517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:39.591674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.289851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:42.933725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:44.614484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.428217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.033712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:49.803975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.325910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:52.918717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.465700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.318507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:57.837277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.456765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.068785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:02.661511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:04.595868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.490309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.203252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:37.918293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:39.677735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.371392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.006106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:44.691992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.519878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.112745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:49.878921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.417914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:52.992833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.543827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.399789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:57.924914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.544629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.147708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:02.744007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:04.676237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.585844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.311032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.000378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:39.782681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.457237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.085412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:44.839839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.601730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.194173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:49.960949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.507798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.074185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.625980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.486419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.001672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.635808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.233126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:02.836498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:04.760872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.685897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.415001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.089126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:39.870697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.547091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.165191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:44.964576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.685814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.281829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.046238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.601008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.156064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.711389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.572887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.085593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.731384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.327193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:02.946471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:04.837231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.759009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.493328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.165170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:39.954767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.625051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.242146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:45.052861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.761348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.384978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.114949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.685317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.228493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.785055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.649698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.154737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.814097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.405874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:03.024608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:04.927842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.838315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.575890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.249791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:40.044863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.742225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.325972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:45.133228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.850287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.471008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.191795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.769935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.303277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.873832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.734047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.234129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.902723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.488466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:03.380880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:05.012526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:34.920485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.663119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.341475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:40.131398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.835419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.406834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:45.217597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:46.933526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.556896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.268180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.862630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.381038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:54.958851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.816841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.308970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:59.990658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.569327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:03.467070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:05.099029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:35.006530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:36.756938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:38.432087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:40.220118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:41.923654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:43.488250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:45.308044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:47.026466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:48.644913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:50.350339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:51.955714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:53.461185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:55.045203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:56.910058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:25:58.394475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:00.082113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:01.654253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:26:03.554965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-24T18:26:10.184886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
(Bleed Enthalpy) - s17(Bypass Ratio) - s15(Bypass-Duct Pressure) (psia) - s6(Corrected Core Speed) (rpm) - s14(Corrected Fan Speed) (rpm) - s13(HPC Outlet Pressure) (psia) - s7(HPC Outlet Static Pressure) (psia) - s11(HPC Outlet Temperature) (â—¦R) - s3(High-Pressure Turbines Cool Air Flow) - s20(LPC Outlet Temperature) (â—¦R) - s2(LPT Outlet Temperature) (â—¦R) - s4(Low-Pressure Turbines Cool Air Flow) - s21(Physical Core Speed) (rpm) - s9(Physical Fan Speed) (rpm) - s8(Ratio of Fuel Flow to Ps30) (pps/psia) - s12CycleEngineRULSetting 1 - c1Setting 2 - c2
(Bleed Enthalpy) - s171.0000.6430.1460.0320.618-0.6740.6980.575-0.6260.6050.678-0.6320.1540.618-0.6840.5530.014-0.6290.0030.013
(Bypass Ratio) - s150.6431.0000.173-0.0190.687-0.7260.7580.613-0.6820.6510.732-0.6780.1150.689-0.7450.5710.021-0.6660.0050.014
(Bypass-Duct Pressure) (psia) - s60.1460.1731.000-0.0440.167-0.1610.1700.118-0.1470.1360.160-0.145-0.0100.162-0.1650.1110.026-0.128-0.0040.014
(Corrected Core Speed) (rpm) - s140.032-0.019-0.0441.000-0.3280.086-0.0660.0290.019-0.019-0.0350.0200.886-0.3260.1010.294-0.044-0.202-0.003-0.019
(Corrected Fan Speed) (rpm) - s130.6180.6870.167-0.3281.000-0.7540.7760.593-0.6760.6490.737-0.677-0.1820.807-0.7770.4580.046-0.573-0.0030.019
(HPC Outlet Pressure) (psia) - s7-0.674-0.726-0.1610.086-0.7541.000-0.806-0.6430.716-0.680-0.7740.715-0.056-0.7550.794-0.578-0.0330.679-0.008-0.017
(HPC Outlet Static Pressure) (psia) - s110.6980.7580.170-0.0660.776-0.8061.0000.670-0.7500.7170.812-0.7500.0830.777-0.8320.6150.025-0.7180.0080.012
(HPC Outlet Temperature) (â—¦R) - s30.5750.6130.1180.0290.593-0.6430.6701.000-0.5990.5760.651-0.6100.1430.592-0.6600.5290.015-0.606-0.0070.009
(High-Pressure Turbines Cool Air Flow) - s20-0.626-0.682-0.1470.019-0.6760.716-0.750-0.5991.000-0.640-0.7270.668-0.111-0.6770.734-0.568-0.0180.653-0.006-0.011
(LPC Outlet Temperature) (â—¦R) - s20.6050.6510.136-0.0190.649-0.6800.7170.576-0.6401.0000.693-0.6430.1040.650-0.7060.5340.014-0.6290.0090.009
(LPT Outlet Temperature) (â—¦R) - s40.6780.7320.160-0.0350.737-0.7740.8120.651-0.7270.6931.000-0.7200.1070.739-0.8000.6050.025-0.7020.0090.017
(Low-Pressure Turbines Cool Air Flow) - s21-0.632-0.678-0.1450.020-0.6770.715-0.750-0.6100.668-0.643-0.7201.000-0.114-0.6770.736-0.570-0.0160.657-0.011-0.010
(Physical Core Speed) (rpm) - s90.1540.115-0.0100.886-0.182-0.0560.0830.143-0.1110.1040.107-0.1141.000-0.179-0.0460.402-0.023-0.322-0.004-0.018
(Physical Fan Speed) (rpm) - s80.6180.6890.162-0.3260.807-0.7550.7770.592-0.6770.6500.739-0.677-0.1791.000-0.7750.4550.043-0.574-0.0030.013
(Ratio of Fuel Flow to Ps30) (pps/psia) - s12-0.684-0.745-0.1650.101-0.7770.794-0.832-0.6600.734-0.706-0.8000.736-0.046-0.7751.000-0.593-0.0310.6930.000-0.010
Cycle0.5530.5710.1110.2940.458-0.5780.6150.529-0.5680.5340.605-0.5700.4020.455-0.5931.0000.058-0.787-0.0060.012
Engine0.0140.0210.026-0.0440.046-0.0330.0250.015-0.0180.0140.025-0.016-0.0230.043-0.0310.0581.0000.058-0.020-0.006
RUL-0.629-0.666-0.128-0.202-0.5730.679-0.718-0.6060.653-0.629-0.7020.657-0.322-0.5740.693-0.7870.0581.000-0.001-0.004
Setting 1 - c10.0030.005-0.004-0.003-0.003-0.0080.008-0.007-0.0060.0090.009-0.011-0.004-0.0030.000-0.006-0.020-0.0011.0000.008
Setting 2 - c20.0130.0140.014-0.0190.019-0.0170.0120.009-0.0110.0090.017-0.010-0.0180.013-0.0100.012-0.006-0.0040.0081.000

Missing values

2024-05-24T18:26:05.218035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-24T18:26:05.451481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EngineCycleSetting 1 - c1Setting 2 - c2(LPC Outlet Temperature) (â—¦R) - s2(HPC Outlet Temperature) (â—¦R) - s3(LPT Outlet Temperature) (â—¦R) - s4(Bypass-Duct Pressure) (psia) - s6(HPC Outlet Pressure) (psia) - s7(Physical Fan Speed) (rpm) - s8(Physical Core Speed) (rpm) - s9(HPC Outlet Static Pressure) (psia) - s11(Ratio of Fuel Flow to Ps30) (pps/psia) - s12(Corrected Fan Speed) (rpm) - s13(Corrected Core Speed) (rpm) - s14(Bypass Ratio) - s15(Bleed Enthalpy) - s17(High-Pressure Turbines Cool Air Flow) - s20(Low-Pressure Turbines Cool Air Flow) - s21RUL
011-0.0007-0.0004641.821589.701400.6021.61554.362388.069046.1947.47521.662388.028138.628.419539239.0623.4190191
1120.0019-0.0003642.151591.821403.1421.61553.752388.049044.0747.49522.282388.078131.498.431839239.0023.4236190
213-0.00430.0003642.351587.991404.2021.61554.262388.089052.9447.27522.422388.038133.238.417839038.9523.3442189
3140.00070.0000642.351582.791401.8721.61554.452388.119049.4847.13522.862388.088133.838.368239238.8823.3739188
415-0.0019-0.0002642.371582.851406.2221.61554.002388.069055.1547.28522.192388.048133.808.429439338.9023.4044187
516-0.0043-0.0001642.101584.471398.3721.61554.672388.029049.6847.16521.682388.038132.858.410839138.9823.3669186
6170.00100.0001642.481592.321397.7721.61554.342388.029059.1347.36522.322388.038132.328.397439239.1023.3774185
718-0.00340.0003642.561582.961400.9721.61553.852388.009040.8047.24522.472388.038131.078.407639138.9723.3106184
8190.00080.0001642.121590.981394.8021.61553.692388.059046.4647.29521.792388.058125.698.372839239.0523.4066183
9110-0.00330.0001641.711591.241400.4621.61553.592388.059051.7047.03521.792388.068129.388.428639338.9523.4694182
EngineCycleSetting 1 - c1Setting 2 - c2(LPC Outlet Temperature) (â—¦R) - s2(HPC Outlet Temperature) (â—¦R) - s3(LPT Outlet Temperature) (â—¦R) - s4(Bypass-Duct Pressure) (psia) - s6(HPC Outlet Pressure) (psia) - s7(Physical Fan Speed) (rpm) - s8(Physical Core Speed) (rpm) - s9(HPC Outlet Static Pressure) (psia) - s11(Ratio of Fuel Flow to Ps30) (pps/psia) - s12(Corrected Fan Speed) (rpm) - s13(Corrected Core Speed) (rpm) - s14(Bypass Ratio) - s15(Bleed Enthalpy) - s17(High-Pressure Turbines Cool Air Flow) - s20(Low-Pressure Turbines Cool Air Flow) - s21RUL
20621100191-0.0005-0.0000643.691610.871427.1921.61551.782388.269068.9048.07519.802388.288143.568.509239838.3923.12189
20622100192-0.00090.0001643.531601.231419.4821.61551.142388.179060.4548.18520.592388.218143.468.489239738.5623.07708
20623100193-0.00010.0002643.091599.811428.9321.61552.042388.299067.5748.19520.112388.198142.028.542439738.4723.02307
20624100194-0.00110.0003643.721597.291427.4121.61551.992388.239068.8548.12519.552388.228139.678.521539438.3823.13246
20625100195-0.0002-0.0001643.411600.041431.9021.61551.422388.239069.6948.22519.712388.288142.908.551939438.1423.19235
20626100196-0.0004-0.0003643.491597.981428.6321.61551.432388.199065.5248.07519.492388.268137.608.495639738.4922.97354
20627100197-0.0016-0.0005643.541604.501433.5821.61550.862388.239065.1148.04519.682388.228136.508.513939538.3023.15943
206281001980.00040.0000643.421602.461428.1821.61550.942388.249065.9048.09520.012388.248141.058.564639838.4422.93332
20629100199-0.00110.0003643.231605.261426.5321.61550.682388.259073.7248.39519.672388.238139.298.538939538.2923.06401
20630100200-0.0032-0.0005643.851600.381432.1421.61550.792388.269061.4848.20519.302388.268137.338.503639638.3723.05220